Rules Engine
The rules engine is the configuration-driven core that checks your shot data and symptoms against sport-specific target windows to identify the best-fitting pattern.
The rules engine is the configuration layer that gives the heuristic engine its sport-specific knowledge. It is built as a set of explicit, version-controlled rules. This means each rule defines a condition (a metric value outside a target window, a symptom pattern, a combination of observable cues), the fault it indicates, the severity of that fault. The recommended initial response. When the engine receives your data, it evaluates your values against each rule and returns the highest-scoring match as the primary finding, with any co-occurring patterns noted as secondary.
Because the rules are explicit and version-controlled, the results are repeatable and inspectable. You can see which threshold a value crossed, which rule that threshold belongs to, and why that rule was assigned its severity level. This traceability is a design requirement, not an afterthought. This means SwingVantage is committed to showing the reasoning behind every finding. The rules engine is what makes that possible. A rule that is incorrect or outdated can be identified, corrected, and re-deployed without retraining a model. The fix is a configuration change that takes effect immediately for all subsequent analyses.
The rules engine is segmented by sport and skill level so the same metric is judged against the right reference. A club speed of 85 mph is evaluated against the target window appropriate for a mid-handicap golfer; it is not compared against a tour-pro benchmark. This segmentation prevents the common problem of non-expert athletes receiving findings calibrated to pro standards. This means which either creates false alarms (flagging values that are entirely appropriate for the athlete's level) or false confidence (reporting values as acceptable when they are actually limiting for the athlete's current ability).
If a finding surprises you, look at the threshold it crossed and the target window for your skill level. Understanding which benchmark your value is being compared against helps you evaluate whether the finding is meaningful for your current stage of development.
If you are reliably at the edge of a threshold. This means right on the boundary of a flagged window. This means pay attention to whether the finding triggers on some sessions and not others. This variability is often more informative than a consistent flag. This means it suggests the fault is present intermittently. This can point to a timing or consistency issue rather than a fundamental technique problem.
Example
A driver spin of 3,400 rpm falls outside the optimal window in the rules engine, which flags it with the exact threshold it crossed.
How it shows up on video
The rules engine output is visible in the fault diagnosis section of your report — in particular in the "Why" explanation that follows each finding. This explanation references the specific threshold or benchmark your data crossed, making the rule-based reasoning transparent.
Common mistakes
- Conflating the rules engine with generic advice — the rules in the engine are calibrated to sport-specific coaching research and benchmarks, not general tips.
- Expecting the rules engine to disagree with itself across sessions. This means by design, the same inputs always produce the same outputs. If findings vary across sessions, the input data (video quality, swing conditions) varied.
- Not updating practice when the rules engine flags the same fault across multiple sessions. This means a recurring fault finding is the engine signaling that the primary issue has not resolved.
- Assuming a finding from the rules engine is less credible than a finding from an AI model. This means rule-based findings are explicitly grounded in coaching knowledge and are fully traceable. AI-augmented findings add narrative depth but are built on top of the rule-based foundation.
In SwingVantage Motion Lab
When you view a finding in SwingVantage, the rules engine is what produced it. The fault label, severity, and threshold information shown on each metric card in your report are all derived from the rules engine's evaluation of your data. The rules are updated periodically as the sport-specific knowledge base is refined — you will see version notes when rules affecting your tracked metrics are updated.
Frequently asked questions
Where do the rules engine's benchmarks come from?
Benchmarks are derived from publicly available sports science research, coaching literature, and skill-level population data — not proprietary or fabricated databases. Each benchmark references its source category, and the methodology page describes how thresholds are set. SwingVantage does not invent benchmarks; it organizes and applies existing expert knowledge.
Can the rules engine handle faults that do not fit a standard pattern?
The rules engine handles the fault patterns it was built with — common, well-documented issues that appear across large populations of athletes. Unusual or compound faults that do not match any standard pattern will produce a low confidence score and may trigger a advice for optional AI-assisted analysis. This can reason about less common patterns that fall outside the rules library.
Related terms
- Deterministic IntelligenceDeterministic intelligence is analysis that follows explicit, written rules, so the same inputs always produce the same answer and you can trace exactly why.
- BenchmarkA benchmark is a reference value that defines acceptable or optimal performance for a given metric, used to judge your data against the right standard for your sport and skill level.
- Heuristic EngineThe heuristic engine is the deterministic, rules-based part of SwingVantage that runs first on every analysis — no external AI call required.
Put this into your swing
SwingVantage can spot this in your own swing — free to start.